{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "import seaborn as sb\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def model_index(file_name):\n",
    "    return int(file_name.split('_')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_leaderboard(file_name, scale=False):\n",
    "    result = []\n",
    "    with open(file_name, \"rt\", encoding='utf-8') as fd:\n",
    "        started = False\n",
    "        for l in fd:\n",
    "            l = l.strip()\n",
    "            if not started:\n",
    "                if l.startswith(\"Leaderboard\"):\n",
    "                    started = True\n",
    "                continue\n",
    "            name, rest = l.split(':')\n",
    "            wld = map(str.strip, rest.split(','))\n",
    "            win, lose, draw = map(lambda s: int(s.split('=')[1]), wld)\n",
    "            # models from the last round played 40% more games, so score need to be compensated\n",
    "            if scale:\n",
    "                win = int(win / 1.4)\n",
    "                lose = int(lose / 1.4)\n",
    "                draw = int(draw / 1.4)\n",
    "            result.append((os.path.basename(name), win, lose, draw))\n",
    "    return result       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semi-final analisys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "v1 = read_leaderboard(\"mu-t5-6-res2.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('best_010_00210.dat', 339, 41, 0),\n",
       " ('best_015_00260.dat', 298, 82, 0),\n",
       " ('best_155_02510.dat', 287, 93, 0),\n",
       " ('best_150_02460.dat', 273, 107, 0),\n",
       " ('best_140_02360.dat', 267, 113, 0),\n",
       " ('best_145_02410.dat', 266, 114, 0),\n",
       " ('best_165_02640.dat', 253, 127, 0),\n",
       " ('best_005_00100.dat', 250, 130, 0),\n",
       " ('best_160_02560.dat', 236, 144, 0),\n",
       " ('best_135_02310.dat', 220, 160, 0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1_pairs = list(map(lambda p: (model_index(p[0]), p[1] / (p[1] + p[2] + p[3])), v1))\n",
    "v1_pairs.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1_index, v1_ratios = zip(*v1_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16a207290>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(v1_ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1_df = pd.DataFrame(data={\n",
    "    \"Wall time\": [0] * len(v1_index), \n",
    "    'Step': v1_index, \n",
    "    'Value': v1_ratios\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1_df.to_csv(\"mu-v1-wins.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
